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Arya, Arti
- GB-NClust:A Pioneering Graph-Based Approach for Natural Clustering in Spatial Datasets
Authors
1 Department of MCA, PES School of Engineering, Bangalore, IN
2 Department of CSE, YMCA Institute of Engineering, Faridabad, Haryana, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 140-146Abstract
The exponential rate at which volume of data is increasing, it is actually impractical to avoid the need of analyzing data for decision-making purposes. Clustering is one of the very effective ways for the data analysis. It is a principal tool of data mining for extracting previously unknown patterns existing in spatial datasets. It is a process of gathering similar data objects into one group such that objects in different groups are dissimilar. Datasets having non-uniform data object distribution are not dealt properly by the available clustering algorithms in literature. In this paper, an innovative approach of generating clusters in spatial datasets (uniform or non-uniform) have been proposed which models dataset using Delaunay structure for capturing spatial proximities and the analysis of the Delaunay edges have been carried out based on a range which is computed automatically by the algorithm for determining whether the two vertices are attracted towards each other or there is a repulsion between them. The edges corresponding to the vertices that are repulsive to each other are removed from the Delaunay structure thus obtaining the desired clusters of arbitrary shapes without any human interaction. The edges are classified as very strong edges, weak edges and floating edges. The dense and sparse clusters present in the same dataset have also been identified effectively even in the presence of bridges between the two clusters. The experimental study is conducted on sample datasets, which shows encouraging results.
Keywords
Clustering, Delaunay Structure, Spatial Proximity, Spatial Datasets.
- Automatic Fuzzy Classification Tool for Customer Loyalty Using Gaussian Membership Function
Authors
1 Department of MCA, PES School of Engineering, Bangalore, IN
2 Department of CSE, PES School of Engineering, Bangalore, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 168-173Abstract
Retaining customers in current era is of utmost importance and crucial for the success of any business enterprise. Identifying potential and loyal customers for any enterprise is of great importance. Customer loyalty is one of the major aspects of Customer Relationship Management (CRM). It is very important for an organization to identify its loyal customers, so that the organization can provide better and special services to these customers in order to enhance their business. In this paper, the construction of fuzzy decision tree has been refined by assigning the weights to each attribute, as all the attributes may not affect the customer loyalty equally.
This paper proposes an application, which makes use of fuzzy decision trees for classifying customers into various categories. Rather than identifying a customer crisply falling near to the end boundaries, soft boundaries (concept of fuzziness) are used which classifies the customer into different predefined classes of loyalty from the information stored about the customers by the organization.
This procedure helps in classifying customers in a better way as compared to crisp classification. The proposed framework is evaluated on a sample dataset that provides encouraging results.